
Traditional method (termed Tr.): This method is to pre-process IMU data using the traditional approaches, i.e., a low-pass filter followed by the intrinsic modeling [1]}. The results are used either standalone for integration or fused with the visual data for long term navigation [2]}.

VINS [3]}: The second one is a representative state-of-the-art visual-inertial odometry method with open-source implementation, which also relies on analytical equations to process IMU measurements.

End-to-end learning (termed EL.) method [4]}: The third one is a competing DNN method by learning the relative position and rotation directly, with our customized RNN implementation.

TLIO [5]}: The fourth method is the EKF and deep learning combined method, which demonstrates high-quality performance in augmented reality applications.

Gyro de-noising [6]}: The last one is a DNN based gyroscope de-noising method for attitude estimation.

